Research

Research Vision: Unveiling the Principles and Regulations of Engineering for Achieving Smart Manufacturing.
Research goal: Promote the democratization of manufacturing through digital innovation and intelligence.

My research is centered around advanced manufacturing science, with a particular emphasis on data-driven design, artificial intelligence-powered material development, the progress of manufacturing processes, and the integration of digitization. In my ‘Digital Manufacturing’ laboratory, we delve into the design, materials, and their processing behavior across micro, meso, and macro scales. Through the generation, collection, and analysis of both experimental and computational data, we aim to achieve a profound understanding of engineering principles, performance and functionality. The goal is to leverage this knowledge to democratize advanced manufacturing processes and systems, thereby revolutionizing the human-machine synergies in manufacturing engineering.

The research marked a shift in the way scientists think about part-process-performance, allowing a synchronized approach between topology, material, and manufacturing/delivery systems stitched with data synchronization which is evaluated in both digital and physical environments. My research aims to provide a manufacturing innovation platform for capturing advanced manufacturing ingenuity in the critical sectors of the US economy (healthcare, aerospace, automotive, and construction industries). The scholarly program objectives are to pursue the scientific questions in manufacturing science, procure major grant funding, to continue collaboration on projects through multidisciplinary teams.

Bio-manufacturing and Tissue Engineering: Khoda, B. et. al. 2021 JMSE; Khoda, B. et. al. 2019 JMP; Khoda, B. et. al. 2018 Materials;
The goal of this research is to develop a rapid manufacturing technique for tissue and cell spheroids as building blocks for tissue engineering research. We developed a novel spheroid fabrication technique with cell-laden bioink by optimizing the hydrogel material composition, including Alginate, CMC, TO-NFC, and nano-clay. The optimized hybrid hydrogel (i) demonstrates >90% cell viability in ~180 ┬Ám filament diameter and (ii) enables >1.2 cm tall scaffold structures with an in-house pneumatic extrusion-based bioprinter. The ongoing research will help us to: (i) build/manufacture functional tissue and organ and (ii) extract bio-molecules through pharming.

Generative Design with Deep-Learning for Manufacturable Topology: Khoda, B. et. al. 2021 3DPAM; Khoda, B. et. al. 2021 Sci. Report; Khoda, B. et. al. 2020 JMSE; Khoda, B et. al. 2017 JMP; Khoda, B et. al. 2017 RCIM
A novel additive metal structure manufacturing process is developed with a continuous rod. The primary objective is to address the unexplored potential in the Ashby chart, particularly focusing on enhancing modulus while reducing density. To achieve solutions to this complex problem, we will employ deep learning techniques for generative design, incorporating Graph Neural Networks (GNN) to harness the power of artificial intelligence (AI).

Selective Particle Delivery with Liquid Career System: Khoda, B. et. al. 2021 JMNM; Khoda, B et. al. 2021 JMSE; Khoda, B. et. al. 2023 Prog. Coating; Khoda, B. et. al. 2022 Appl. Mechanics; Khoda, B. et. al. 2023 Appl. Mechanics
Delivering granular material in hard-to-reach porous structure is a challenge, which can enable the manufacturing of next-generation materials and devices, including energy storage, tubular structures, synthetic blood vessels, tissue scaffolds, flexible electronics, filtrations, and meta-surfaces regulating optical, acoustic, and magnetic waves. For the first time, my lab reported particle transfer by entrapment process from non-colloidal mixture. Both analytical and numerical models will be developed which will be trained and validated with experimental data to generate a deep learning model. The research goal is to establish the selective particle entrapment process as an advanced manufacturing technology.

PFAS Removal with Functionalized and Patterned Porous Structure:
Per- and polyfluoroalkyl substances (PFAS) are commonly found in agricultural water and PFAS can be released into the irrigation water and food processing cycles, thus causing adverse effects on human health and the environment. Current technologies for the removal and treatment of PFAS either have insufficient selectivity and efficiency in degrading/removing PFAS or resource expensive. To fill the gaps, the primary objective of this project is to seamlessly integrate AI-based computational design, advanced manufacturing techniques and Metal-Organic Frameworks (MOF) nano-material technology to develop a tailored PFAS filtration platform which will be targeted for selective PFAS removal in a high flow-rate environment and accessible to small, disconnected communities. This proposed platform offers adjustability at macro, micro, and nano scales, presenting potential advances in materials and manufacturing processes.

Resource Efficiency in Additive Technology: Khoda, B. et. al. 2020 JMSE; Khoda, B et. al. 2018 RPJ; Khoda, B et. al. 2017 JMP; Khoda, B et. al. 2017 RCIM
The goal of this research is to create process behaviors analytics for solid and cellular porous 3D-printed objects. One of the major constraints of additive manufacturing processes is that they consume a significant amount of resources (i.e., time, energy and material, support structure and cost) to fabricate parts, which is often tied to the part and process attributes. The objective is to establish a relationship among design, geometry, process variables, material distribution, and AM capabilities while establishing resource consumption mechanisms. This research is built upon balancing the hierarchical AM eco-system with primary emphasis on the pre-processing stage followed by downstream optimization.

Porous infill design and 3D printing: Khoda, B. et. al. 2021 JMSE; Khoda, B. et. al. 2018 RPJ; Khoda, B. et. al. 2023 ASME Eng.
A new fabrication pattern for honeycomb infill is proposed for additive manufacturing applications. The proposed pattern will uniformly distribute the material and can accommodate controllable variational honeycomb infill while maintaining continuity with relative ease. The infill structures are fabricated with both uniform and variational patterns, which are then compared with the traditional tool-path pattern with compression testing. The results show that the proposed design demonstrates uniform densification under compression and performs better while absorbing more energy. Studying novel pattern and their impact on mechanical properties will help understand the design-performance relationship of the 3D printed parts.